Senior Research Engineer, Controls

What you'll do

• Design and enhance control algorithms combining MPC with learning-based approaches.
• Build data, training, and evaluation tools for online control optimization, with attention to real-time and compute-constrained operation.
• Plan and conduct vehicle testing, analyze performance, and debug issues across simulation and public-road environments.

What they ask for

• Master's or PhD degree in Computer Science, Mechanical Engineering, Robotics, Aerospace Engineering, or a related field.
• 2+ years of MLE experience or industry experience designing and developing for robotics applications.
• Strong foundation in motion control and modern neural network architectures, with expertise in at least one area such as IL/RL, time-series analysis, or dynamic system modeling.
• Linux debugging skills, strong Python and C++ programming expertise, experience developing and training models with modern frameworks such as PyTorch, and hands-on familiarity with data ingestion and processing pipelines.

Nice to have

• Hands-on skills in adaptive and nonlinear control, MPC and optimal control, robust control, data-driven control, or Kalman filters.
• Understanding of AV control, vehicle dynamics, and drive-by-wire systems; or expertise applying, verifying, and validating ADAS/autonomous driving features and functions.

Pay

USD 150,000–200,000 (per-year-salary)

Tools

MPC, DL, RL, IL, Linux, Python, C++, PyTorch

AI focus

Develop machine-learning vehicle models and learning-based control policies, including DL/RL/IL approaches, for autonomous vehicle motion control.